• Journal of Practical Engineering Education
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• v.14 no.1
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• pp.219-224
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• 2022

Q-Learning is a technique widely used as a basic algorithm for reinforcement learning. Q-Learning trains the agent in the direction of maximizing the reward through the greedy action that selects the largest value among the rewards of the actions that can be taken in the current state. In this paper, we studied a policy that can speed up agent training using Q-Learning in Frozen Lake 8×8 grid environment. In addition, the training results of the existing algorithm of Q-learning and the algorithm that gave the attribute 'direction' to agent movement were compared. As a result, it was analyzed that the Q-Learning policy proposed in this paper can significantly increase both the accuracy and training speed compared to the general algorithm.

• English Language & Literature Teaching
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• v.16 no.4
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• pp.39-69
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• 2010

This study aims to identify how participants in online-incorporated English learning perceive interaction between achievement and factors of learning and personality. Using grounded theory analysis, this study attempts to generate a theoretical model depicting how the factors work with the L2 learners situated in the learning setting. A total of 231 college freshmen participated in online and offline EFL learning programs for the duration of one semester. In addition, all respondents completed a survey questionnaire on their learning experiences. In the investigation of the differences between low- and high-proficiency groups, audio-taped interviews with 20 selected students, 10 from each group, have revealed differences not only in the types of personal and instructional factors, but also, more importantly, in the interrelationship between these factors in each group's learning model. These models effectively explained the statistically significant differences in four questionnaire items, such as online learning and contributions of offline class sections to their L2 achievement. These findings entail L2 practitioners' shared understandings of their students' perspectives of learning in the specific L2 learning context.

• The Journal of Korea Robotics Society
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• v.18 no.1
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• pp.53-58
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• 2023

In general, in order to operate an autonomous underwater vehicle (AUV) in an underwater environment, a navigation system such as a Doppler Log (DVL) using a Doppler phenomenon of ultrasonic waves is used for speed and direction estimation. However, most of the ultrasonic sensors in underwater is large for long-distance sensing and the cost is very high. In this study, not only canal neuromast on the fish's lateral lines but also superficial neuromast are studied on the simulation to obtain pressure values for each pressure sensor, and the obtained pressure data is supervised using spherical CNN. To this end, through supervised learning using pressure data obtained from a pressure sensor attached to an underwater vehicle, we can estimate the speed and angle of the underwater vehicle in a quasi-static underwater environment and propose a method for a non-ultrasonic based navigation system.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.321-340
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• 2017

The purpose of this study is to design a teaching model applying a problem-based learning model and to analyze the educational benefits that students felt. This study initiated a problem-based learning model from an analysis of existing studies. Through the consultation of experts, the scenario was modified. The problem was designed according to the design stage activity (problem analysis, PBL class suitability judgment, contents analysis, learner analysis, environment analysis, PBL operating environment decision, PBL class) and Strategic Design (problem situation design, learning resource design, Facilitation design, operational strategy design, evaluation design, PBL operating environment design). Based on the initial scenarios, the researcher analyzed the results of the problem - based learning through learners' reflective diaries. The researcher was able to confirm that the critical thinking and creativity were improved in the first PBL problem situation, and the method for smooth communication and cooperation was utilized. The results on analyzing the effects of education about the first problem-based learning and students' opinions about modification will be used for the second revision and supplement of the course design. This study introduces a case of PBL course development and expects further application and research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4244-4274
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• 2021

The utilization of UAVs in various fields has led to the development of flying ad hoc network (FANET) technology. In a network environment with highly dynamic topology and frequent link changes, the traditional routing technology of FANET cannot satisfy the new communication demands. Traditional routing algorithm, based on geographic location, can "fall" into a routing hole. In view of this problem, we propose a geolocation routing protocol based on multi-agent reinforcement learning, which decreases the packet loss rate and routing cost of the routing protocol. The protocol views each node as an intelligent agent and evaluates the value of its neighbor nodes through the local information. In the value function, nodes consider information such as link quality, residual energy and queue length, which reduces the possibility of a routing hole. The protocol uses global rewards to enable individual nodes to collaborate in transmitting data. The performance of the protocol is experimentally analyzed for UAVs under extreme conditions such as topology changes and energy constraints. Simulation results show that our proposed QLGR-S protocol has advantages in performance parameters such as throughput, end-to-end delay, and energy consumption compared with the traditional GPSR protocol. QLGR-S provides more reliable connectivity for UAV networking technology, safeguards the communication requirements between UAVs, and further promotes the development of UAV technology.

• Journal of the Korean Institute of Educational Facilities
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• v.18 no.5
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• pp.43-50
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• 2011

There are emerging issues to update the educational environment for schools in terms of information and communication technology in order to provide customized programs to students as well as all participants relating to learning and teaching. The past year has been turbulent as the education facilities has changed and new procurement processes such as BTL have emerged. In this study, the feasibility analysis of ICT for the public educational environment is to analyse the current primary schools by means of collecting parent's opinion. In the web-site questionnaires, it was designed with 70 items such as teaching method, class organization, aptitude drill and educational community. As results, the statistical analysis is to propose the list of priority and orientation covering social agenda in the issue of ICT for education, the benefits schools can achieve by smart environment is to have the advanced learning services and solutions that represents parental engagement with identical local aims of interactive interface between their students and qualified teachers at a school. Both the national curriculum as well as the after-school program initiatives from the ministry of education, science and technology may reduce negative effects of private education so that the program has to be carefully developed for balanced education society revitalizing mutual communication within regional learning participants such as students, teachers and educational experts.

• Journal of Korean Clinical Health Science
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• v.11 no.1
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• pp.1607-1615
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• 2023

purpose: In addition to evaluating the quality of sleep of college students, the effect on non-face-to-face online learning satisfaction is identified and used as basic data for improving the quality of remote lectures. Methods: From June 1 to June 24, 2022, a self-entry survey was conducted on students enrolled in the dental hygiene department of D University in Daegu. To evaluate the non-face-to-face online learning satisfaction and sleep quality of the study subjects using the lBM SPSS Statistics 21 program, ANOVA analysis was conducted on the difference between individual stress levels and non-face-to-face online learning satisfaction. The correlation between sleep quality, stress, and non-face-to-face online learning satisfaction was analyzed using Pearson's correlation coefficient. Results: The lower the quality of sleep, the higher the stress, resulting in statistically significant results (p<0.001). The higher the quality of sleep, the higher the learning satisfaction, resulting in statistically significant results (p<0.001). There was a statistically significant positive correlation between learning satisfaction and stress (r=0.591, p<0.01). Conciussions: Through the above results, in order to improve the satisfaction of non-face-to-face online learning, it is necessary to manage the individual's learning environment and health to relieve stress. Instructors also need to communicate with learners and apply teaching methods considering learners' academic abilities.

• Hwankyungkyoyuk
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• v.21 no.3
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• pp.1-12
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• 2008

This study is aimed at suggesting ways to develop field trip or learning materials focusing on environment of Jeju seashore in order to make an effective field trip. To perform these purposes, the contents and concepts were analyzed from environment-related 'geological unit' of elementary science textbook. Afterwards, the places having the geological features in coincidence with them are chosen, and investigated, and these regions can develop into geological teaming places for field trip. Each teaming spot focuses on understanding and finding out the characteristic geological environment of rock shore, gravel shore, sand shore, shellfish shore, and tideland shore among Jeju shores. When field trip is conducted at the preparatory stage, students can get advance knowledge on geological concepts from textbook. The activity record paper is presented at the field trip stage where students observe geological phenomena on their own. After field trip is finished, the summary stage is given to solve some problems on the basis of the observed contents. The developed data from this research have its regional limits, but is surely useful for teachers who try to plan field trip when they especially choose the right field trip spots, or plan to make the process for field trip preparation of the environmental education. Furthermore, with this survey and activities, students can take the chance to improve the learning effect through their own experience on environment of Jeju seashore.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.751-770
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• 2019

Action recognition is an essential task in computer vision due to the variety of prospective applications, such as security surveillance, machine learning, and human-computer interaction. The availability of more video data than ever before and the lofty performance of deep convolutional neural networks also make it essential for action recognition in video. Unfortunately, limited crafted video features and the scarcity of benchmark datasets make it challenging to address the multi-person action recognition task in video data. In this work, we propose a deep convolutional neural network-based Effective Hybrid Learning (EHL) framework for two-person interaction classification in video data. Our approach exploits a pre-trained network model (the VGG16 from the University of Oxford Visual Geometry Group) and extends the Faster R-CNN (region-based convolutional neural network a state-of-the-art detector for image classification). We broaden a semi-supervised learning method combined with an active learning method to improve overall performance. Numerous types of two-person interactions exist in the real world, which makes this a challenging task. In our experiment, we consider a limited number of actions, such as hugging, fighting, linking arms, talking, and kidnapping in two environment such simple and complex. We show that our trained model with an active semi-supervised learning architecture gradually improves the performance. In a simple environment using an Intelligent Technology Laboratory (ITLab) dataset from Inha University, performance increased to 95.6% accuracy, and in a complex environment, performance reached 81% accuracy. Our method reduces data-labeling time, compared to supervised learning methods, for the ITLab dataset. We also conduct extensive experiment on Human Action Recognition benchmarks such as UT-Interaction dataset, HMDB51 dataset and obtain better performance than state-of-the-art approaches.

• Korean Journal of Radiology
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• v.22 no.12
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• pp.2073-2081
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• 2021

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.